|TensorFlow 1 version||View source on GitHub|
Compat aliases for migration
See Migration guide for more details.
tf.CriticalSection( name=None, shared_name=None, critical_section_def=None, import_scope=None )
CriticalSection object is a resource in the graph which executes subgraphs
in serial order. A common example of a subgraph one may wish to run
exclusively is the one given by the following function:
v = resource_variable_ops.ResourceVariable(0.0, name="v") def count(): value = v.read_value() with tf.control_dependencies([value]): with tf.control_dependencies([v.assign_add(1)]): return tf.identity(value)
Here, a snapshot of
v is captured in
value; and then
v is updated.
The snapshot value is returned.
If multiple workers or threads all execute
count in parallel, there is no
guarantee that access to the variable
v is atomic at any point within
any thread's calculation of
count. In fact, even implementing an atomic
counter that guarantees that the user w